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Tracking Object Existence From an Autonomous Patrol Vehicle

Monday, 26 September 2011

These techniques could be part of a mobile surveillance system attached to a ground vehicle,
boat, or airplane.

An autonomous vehicle patrols a large region, during which an
algorithm receives measurements of detected potential objects
within its sensor range. The goal of the algorithm is to track all
objects in the region over time. This problem differs from traditional
multi-target tracking scenarios because the region of interest
is much larger than the sensor range and relies on the movement
of the sensor through this region for coverage. The goal is
to know whether anything has changed between visits to the same
location. In particular, two kinds of “alert” conditions must be
detected: (1) a previously detected object has disappeared and (2)
a new object has appeared in a location already checked.

For the time an object is within sensor range, the object can
be assumed to remain stationary, changing position only
between visits. The problem is difficult because the upstream
object detection processing is likely to make many errors,
resulting in heavy clutter (false positives) and missed detections
(false negatives), and because only noisy, bearings-only
measurements are available. This work has three main goals:
(1) Associate incoming measurements with known objects or
mark them as new objects or false positives, as appropriate.
For this, a multiple hypothesis tracker was adapted to this
scenario.
(2) Localize the objects using multiple bearings-only measurements
to provide estimates of global position (e.g., latitude
and longitude). A nonlinear Kalman filter extension provides
these 2D position estimates using the 1D measurements.
(3) Calculate the probability that a suspected object truly exists
(in the estimated position), and determine whether alert
conditions have been triggered (for new objects or disappeared
objects). The concept of a “probability of existence”
was created, and a new Bayesian method for updating
this probability at each time step was developed.

A probabilistic multiple hypothesis approach is chosen
because of its superiority in handling the uncertainty arising
from errors in sensors and upstream processes. However, traditional
target tracking methods typically assume a stationary
detection volume of interest, whereas in this case, one must
make adjustments for being able to see only a small portion of
the region of interest and understand when an “alert” situation
has occurred. To track object existence inside and outside the
vehicle’s sensor range, a probability of existence was defined for
each hypothesized object, and this value was updated at every
time step in a Bayesian manner based on expected characteristics
of the sensor and object and whether that object has been
detected in the most recent time step. Then, this value feeds into
a sequential probability ratio test (SPRT) to determine the “status”
of the object (suspected, confirmed, or deleted). Alerts are
sent upon selected status transitions. Additionally, in order to
track objects that move in and out of sensor range — and update
the probability of existence appropriately — a variable “probability
detection” has been defined and the hypothesis probability
equations have been re-derived to accommodate this change.

Unsupervised object tracking is a pervasive issue in automated
perception systems. This work could apply to any mobile platform
(ground vehicle, sea vessel, air vehicle, or orbiter) that intermittently revisits regions of interest and needs
to determine whether anything interesting
has changed.

This work was done by Michael Wolf and
Lucas Scharenbroich of Caltech for NASA’s Jet
Propulsion Laboratory. For more information,
contact This email address is being protected from spambots. You need JavaScript enabled to view it.. NPO-47274

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